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# Generate the sample probability
def sample_prob(self, probs):
return tf.nn.relu(tf.sign(probs - tf.random.uniform(tf.
shape(probs))))
We will need functions to reconstruct the input:
Chapter 10
def rbm_reconstruct(self,X):
h = tf.nn.sigmoid(tf.matmul(X, self.w) + self.hb)
reconstruct = tf.nn.sigmoid(tf.matmul(h, tf.transpose(self.w)) +
self.vb)
return reconstruct
To train the RBM created we define the train() function. The function calculates
the positive and negative grad term of contrastive divergence and uses the weight
update equation to update the weights and biases:
# Training method for the model
def train(self, X, epochs=10):
loss = []
for epoch in range(epochs):
#For each step/batch
for start, end in zip(range(0, len(X), self.
batchsize),range(self.batchsize,len(X), self.batchsize)):
batch = X[start:end]
#Initialize with sample probabilities
self.hb))
self.vb))
h0 = self.sample_prob(self.prob_h_given_v(batch, self.w,
v1 = self.sample_prob(self.prob_v_given_h(h0, self.w,
h1 = self.prob_h_given_v(v1, self.w, self.hb)
#Create the Gradients
positive_grad = tf.matmul(tf.transpose(batch), h0)
negative_grad = tf.matmul(tf.transpose(v1), h1)
#Update learning rates
self.w = self.w + self.learning_rate *(positive_grad -
negative_grad) / tf.dtypes.cast(tf.shape(batch)[0],tf.float32)
self.vb = self.vb + self.learning_rate * tf.reduce_
mean(batch - v1, 0)
self.hb = self.hb + self.learning_rate * tf.reduce_
mean(h0 - h1, 0)
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